| 31. |
- Olsson, Sara K, et al.
(författare)
-
Cerebrospinal fluid kynurenic acid is associated with manic and psychotic features in patients with bipolar I disorder.
- 2012
-
Ingår i: Bipolar disorders. - : Wiley. - 1399-5618 .- 1398-5647. ; 14:7, s. 719-26
-
Tidskriftsartikel (refereegranskat)abstract
- Olsson SK, Sellgren C, Engberg G, Landén M, Erhardt S. Cerebrospinal fluid kynurenic acid is associated with manic and psychotic features in patients with bipolar I disorder. Bipolar Disord 2012: 14: 719-726. © 2012 The Authors. Journal compilation © 2012 John Wiley & Sons A/S. Objectives: Kynurenic acid (KYNA), an end metabolite of tryptophan degradation, antagonizes glutamatergic and cholinergic receptors in the brain. Recently, we reported elevated levels of cerebrospinal fluid (CSF) KYNA in male patients with bipolar disorder. Here, we investigate the relationship between symptomatology and the concentration of CSF KYNA in patients with bipolar I disorder. Methods: CSF KYNA levels from euthymic male {n=21; mean age: 41years [standard deviation (SD)=14]} and female [n=34; mean age: 37years (SD=14)] patients diagnosed with bipolar I disorder were analyzed using high-performance liquid chromatography (HPLC). Results: Euthymic bipolar I disorder patients with a lifetime occurrence of psychotic features had higher CSF levels of KYNA {2.0nm [standard error of the mean (SEM)=0.2]; n=43} compared to patients without any history of psychotic features [1.3nm (SEM=0.2); n=12] (p=0.01). Logistic regression, with age as covariate, similarly showed an association between a history of psychotic features and CSF KYNA levels [n=55; odds ratio (OR)=4.9, p=0.03]. Further, having had a recent manic episode (within the previous year) was also associated with CSF KYNA adjusted for age (n=34; OR=4.4, p=0.03), and the association remained significant when adjusting for a lifetime history of psychotic features (OR=4.1, p=0.05). Conclusions: Although the causality needs to be determined, the ability of KYNA to influence dopamine transmission and behavior, along with previous reports showing increased brain levels of the compound in patients with schizophrenia and bipolar disorder, may indicate a possible pathophysiological role of KYNA in the development of manic or psychotic symptoms.
|
|
| 32. |
- Ortiz, A., et al.
(författare)
-
Episode forecasting in bipolar disorder : Is energy better than mood?
- 2018
-
Ingår i: Bipolar Disorders. - : Blackwell Publishing Inc.. - 1398-5647 .- 1399-5618. ; 20:5, s. 470-476
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders. Methods: We employed three different techniques: entropy calculations and two different machine learning approaches (genetic programming and Markov Brains as classifiers) to determine whether mood, energy or sleep was the best predictor to forecast a mood episode in a physiological time series. Results: Evening energy was the best predictor for both manic and depressive episodes in each of the three aforementioned techniques. This suggests that energy might be a better predictor than mood for forecasting mood episodes in bipolar disorder and that these particular machine learning approaches are valuable tools to be used clinically. Conclusions: Energy should be considered as an important factor for episode prediction. Machine learning approaches provide better tools to forecast episodes and to increase our understanding of the processes that underlie mood regulation. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
|
|
| 33. |
|
|
| 34. |
|
|
| 35. |
|
|
| 36. |
|
|
| 37. |
|
|
| 38. |
- Rolstad, Sindre, 1976, et al.
(författare)
-
Polymorphisms of dopamine pathway genes NRG1 and LMX1A are associated with cognitive performance in bipolar disorder
- 2015
-
Ingår i: Bipolar disorders. - : Wiley. - 1399-5618 .- 1398-5647. ; 17:8, s. 859-868
-
Tidskriftsartikel (refereegranskat)abstract
- LIM homeobox transcription factor 1, alpha (LMX1A) and neuregulin 1 (NRG1) are susceptibility genes for schizophrenia that have been implicated in the dopaminergic pathway and have been associated with altered cognitive functioning. We hypothesized that single nucleotide polymorphisms (SNPs) in LMX1A and NRG1 would be associated with cognitive functioning in bipolar disorder.
|
|
| 39. |
|
|
| 40. |
- Wingard, Louise, et al.
(författare)
-
Initiation and long-term use of benzodiazepines and Z-drugs in bipolar disorder
- 2018
-
Ingår i: Bipolar Disorders. - : Wiley. - 1398-5647 .- 1399-5618. ; 20:7, s. 634-646
-
Tidskriftsartikel (refereegranskat)abstract
- ObjectivesIncreasing evidence points to the harmful effects of long‐term benzodiazepine treatment. Our objective was to study the incidence of, and predictors for, long‐term use of benzodiazepines and Z‐drugs in bipolar disorder.MethodsWe conducted a population‐based cohort study, using data from Swedish national registers. Swedish residents aged 18‐75 years with a recorded diagnosis of bipolar disorder or mania between July 2006 and December 2012, and no history of benzodiazepine/Z‐drug use in the past year, were included. Patients were followed for 1 year with regard to prescription fills of benzodiazepines/Z‐drugs. Initiators were followed for another year during which continuous use for >6 months was defined as “long‐term”. Patient and prescription characteristics were investigated as potential predictors for long‐term use in multivariate logistic regression models.ResultsOut of the 21 883 patients included, 29% started benzodiazepine/Z‐drug treatment, of whom one in five became long‐term users. Patients who were prescribed clonazepam or alprazolam had high odds for subsequent long‐term use (adjusted odds ratios [aORs] 3.78 [95% confidence interval (CI) 2.24‐6.38] and 2.03 [95% CI 1.30‐3.18], respectively), compared to those prescribed diazepam. Polytherapy with benzodiazepines/Z‐drugs also predicted long‐term use (aOR 2.46, 95% CI 1.79‐3.38), as did age ≥60 years (aOR 1.93, 95% CI 1.46‐2.53, compared to age <30 years), and concomitant treatment with psychostimulants (aOR 1.78, 95% CI 1.33‐2.39).ConclusionsThe incidence of subsequent long‐term use among bipolar benzodiazepine initiators is high. Patients on clonazepam, alprazolam or benzodiazepine/Z‐drug polytherapy have the highest risk of becoming long‐term users, suggesting that these treatments should be used restrictively.
|
|
